Due to recent technological advances, large multi-modal datasets are being produced; these include imaging as well as multi-platform genomics data. In complex disease systems such as cancer, integrative analyses of such data can reveal important biological insights into specific disease mechanisms and its subsequent clinical translation [1]}, [2]}. Genomic profiling technologies, such as microarrays, next-generation sequencing, methylation arrays and proteomic analyses, have facilitated thorough investigations at the molecular level. Such multi-platform genomic data resources have been used to develop models to better understand the molecular characterization across cancers and especially in gliomas [3]}, [4]}. While genomic data provides information on the molecular characterization of the disease, radiological imaging, such as magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET), provide complementary information about the structural aspects of the disease. In this context, radiomic analysis involves mining and extraction of various types of quantitative imaging features from different modalities obtained through high-throughput radiology images [5]}. These image-derived features describe various characteristics such as morphology and texture, among others. Integrative analyses of genomic and radiomic features, commonly referred to as radiogenomic analysis, capture complementary characteristics of the underlying tumor [6]}, [7]}. However, such integrative modeling approaches present multiple analytical and computational challenges including incorporating complex biological structure, and high-dimensionality of quantitative imaging/genomic markers, necessitating principled and biologically-informed dimension reduction and information extraction techniques.
